Modern dairy farms generate massive amounts of data.
Automated Milking Systems (AMS) and Precision Livestock Farming (PLF) send anywhere from 40 to as many as 100 alerts daily regarding animal activity, health, feed intake, or milking performance. In theory, this is a huge advantage.
In practice, however, 80% of them are information noise that does not translate into any real economic benefit. Farmers react to irrelevant signals, wasting time and money, and notice real problems too late. The problem lies not in the technology, but in the lack of prioritization.
The systems do exactly what they were designed to do detect deviations from the norm. However, the algorithm cannot distinguish whether we are dealing with a real threat, natural biological variability, or a measurement error.
All signals flow into a single stream. As a result, a classic decision bottleneck arises a phenomenon well-known from the Precision Livestock Farming conference. The farmer sees more than ever before, but instead of planning and managing the herd, they are putting out fires.
Decisions become reactive rather than strategic. As analyses by Wageningen University & Research, the Journal of Dairy Science, Teagasc, and the University of Wisconsin-Madison Dairy Science indicate the problem is not a lack of data, but its proper classification and interpretation in the context of a specific farm. The solution is simpler than it seems.
There is no need to implement additional systems or collect even more information. The key lies in selecting alerts and structuring them for decision-making. A consistent division into three categories is sufficient:
- Critical – require an immediate response (e.g., clear signs of illness, a drastic drop in feed intake).
- Observation – should be monitored over time, but does not necessarily require immediate action.
- Informational – require no intervention (noise).
On one of the farms we analyzed, the system generated over 60 alerts per day. After implementing a simple selection process, the number of actual decisions dropped to 5. The result? Improved reproductive performance and a significant reduction in unnecessary interventions within just 30 days.
It is not a matter of the quantity of data, but the quality of decisions. Modern dairy farming is no longer a race to collect the most information. It has become a decision-management system. PLF systems and milking robots remain extremely precise tools but only tools.
Without the right decision-making structure, they only increase the noise without improving management quality. Only when the signal is separated from the noise does technology truly begin to work toward the farm’s economic results.
You don’t lose money because of a lack of data. You lose it because of decisions made based on bad signals.
Łukasz, CEO, Optimal Dairy™